ES, version 2022-04-08 /
- A large amount of biomedical knowledge is in principle already
available but the question is now how to use the knowledge in a more
- Decision are not always optimal in situations where there is not
enough time, less than 5 minutes per patient or limited
qualifications on site.
- Assisted Intelligence, helping but not replacing the local
responsible health professional.
- Assistance for differential diagnoses in an interactive way.
- Task oriented approach:
- The goal is to try to solve the problems of the patient. Think
you are in an emergency department. A new patient arrive. You
heave only a few information and you have to take the first
decisions, according to meaningful priorities.
- Up to now many "medical record" systems where only describing
what did happen for statistical purposes. Of course already very
useful and making at least the patient information available for
the doctor who has to take decisions. But on itself not much
contribution to the next decisions !
- In general when a new patient arrives few information is
available. Only a few major complaints or abnormal findings.
- In order to help patient, the very first common sense question
is the identification of problems. The identification of
Problems is essential. A problem is here any kind of issue
requiring attention, as well abnormal complaints or findings to
be understood. When a Problem is well understood it may
become a diagnose.
- Given the current set of symptoms and observations, evaluate
the likelihood of one or more Problems or "Health Issues".
- At any time the Problem List must be maintained up-to-date.
The problem list of the patient could be sorted in different
ways according to:
- Probability of the presence of the problem.
- Potential severity.
- At any iteration when new information become available,
the problem list should be re-evaluated. The probability of
some problems may increase or decrease.
- When a problem is confirmed it is usually called a
- The initial approach is simply to try to understand the
reasoning process of experienced clinicians. Up to now
intuitive decisions of experienced doctors could achieve
good results. This analysis should be represented as graphs
explaining the diverse arguments which did lead to the
identification of problems. Anyhow graphs will facilitate
the discussions between several medical experts.
- In the future the approach could become to try to make
more precise decision schema, taking account of the very
large body of scientific knowledge nowadays available.
- Precision of problems:
- Possibility of several degrees of granularity. For example
an "infectious syndrome" when there is probably an
infection while having not yet any idea about location and
the infectious agent. Decision schema could include
intermediary steps in hierarchies.
- "Actions"Evaluation of possible actions in function of the
likelihood of problems:
- Given some "Problem" different types of Action may be
- Recommend to require more information in order to confirm
or exclude the considered Problem.
- Recommend to prescribe directly some treatments.
- Depending on the likelihood of a problem:
- If very low:
- The problem will be considered as excluded and no
further action is recommended.
- If intermediary probability:
- As far as possible new information should be explored,
by more questions to the patient, more examinations
and/or more technical tests.
- Since it would never be possible to explore all the
possible kinds of information (>10.000) it is
necessary to make a selection of the most useful
requests, as well to evaluate their relative priorities.
- Timing, in principle actions are always expected to
provide a result soon or later, typically:
- Immediately e.g. for a question to the patient, of
for a blood pressure measurement.
- After a few hours in case of a lab test. Sometime
a reminder may be necessary.
- After weeks as for example in case of
prescriptions. If no news, in general good news are
assumed, although the worse cannot be excluded ?
- Goals of treatment recommendations:
- Life expectation.
- Quality of life, for example in case of palliative
- If very high:
- The problem is considered to become a confirmed
diagnose and the possibilities of treatments must be
considered based on:
- Expected advantages.
- Risks and costs.
- Level of certainty of the diagnose:
- Objectives of treatments:
- Priorities for life expectation or comfort ?
- Recommendation list:
- The recommendation list will be sorted on relative
priorities. Normally the action proposed at the top of the
list will be the next action, but the user does not
necessarily validate actions as proposed.
- Multiple factors:
- In general multiple factors of decision will be found in the
- These factors may have different importance or weights,
represented by coefficients, in principle normalized from 0. to
- More than one kind of weight could be relevant.
- Asymmetry, for example the absence of an Observation may be
discriminant, while its presence would not be much useful.
- Uncertainty is everywhere and recommendations are based on
- The evaluation of incomplete lists of multiple factors of
different weights will require "Fuzzy
Logic". Thresholds are critical and must be defined.
Trade-off have to be made.
- Keep in mind that problems are not exclusive. Sometimes more
than one problem may exist at the same time.
- Any unexplained finding must be explored.
- As far as possible the recommendation must be explained, with
the arguments leading to the conclusion.
- The obligation to formulate explicitly your current vision
about a case is on itself a factor of care quality.
- Improvement of the collaboration with colleagues in charge of
a common patient. Different opinion may appear and stimulate
- Indirectly the transparency is a contribution to continued
- Responsibility issues:
- The final responsibility relies on the healthcare professional
directly in charge of the patient.
- The system is trustworthy and reliable as far as the user
trust the authors and maintainers of the knowledge base.
- As far as possible the knowledge itself should not reside
inside the "decision engine", but in the knowledge database
discussed in an other chapter.
- The maintainers of the knowledge base are expected to provide
the best possible synthesis of current knowledge.
- Decision engine:
- The idea is to split the developments in 2 branches:
- ( 1 ) Here the decision engine:
- Creation of a set of generic rules intended be steered
by the the knowledge graph data base. As far as
possible the decision engine should be reusable in
- The general requirements will be defined by medical
experts and here data scientists and software developer
will have to find solutions about how to manage the
- Human feeling could sometimes still be included in the
decision process, as one of the factors with a relative
- ( 2 ) A separated chapter of the project will be dealing
with the content of the knowledge
- This page look first at a common decision engine. This could
be extended with specific rules in function with the objective
i.e. finding the most likely diagnose or seeking the most
- Activation of decision procedures:
- Every time when new information comes in, a new evaluation of
the situation is necessary. This should be propagated at
- External decision modules:
- Possibility to delegate some decision processes. For example:
- Given row ECG signal ask interpretation by an external
- Given images ask if to be seen as normal or not.
- The result of such an external procedure is then considered as
- Pure Artificial Intelligence:
- It is not yet clear how far pure AI could be useful in
healthcare, when not taking account of any bio-medical
- Artificial Intelligence works well when a very large base of
reference is available. In practice individual patients
are mostly different. At the end the reference material is
usually based on human experts.
- Needed resources:
- Decision support require graph of both medical knowledge and
- Expertise in medicine and data science.
- Analysis of the current situation:
- Traditional decision strategies currently in use by doctors,
decision trees, scores, ...
- Begin to make a very simple prototype limited to a few
symptoms and disease, which can be presented as a visual graph.
- Take account of relationships of different weights.
- Recommendation of more investigations